Text Generation
MLX
Safetensors
English
pretraining
from-scratch
small-language-model
post-training
silicon
Instructions to use OpenSML/OpenSML-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenSML/OpenSML-150M with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OpenSML/OpenSML-150M") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use OpenSML/OpenSML-150M with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "OpenSML/OpenSML-150M" --prompt "Once upon a time"
- Atomic Chat
Download SHA256SUMS from OpenSML/OpenSML-150M: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
-
https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/SHA256SUMS
- Command line
-
hf download hf://OpenSML/OpenSML-150M/SHA256SUMS
-
curl -L -o SHA256SUMS https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/SHA256SUMS
1.32 kB
| 6050506f4e9411f7720754591436ec5477ee04d8c136f69dedb5365280d73a2e INFERENCE_VERIFICATION.json | |
| cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30 LICENSE | |
| 7edb9a16eaf1ef9232bf5eb3b82dd6a6e5df36fe63108d1525bce00777025bdc README.md | |
| b5483cebea706f876ff0deddf843e046e0e963ff76e6d97b18d521da03ebeae5 checkpoint_provenance.json | |
| 6d0fc4cb42c9fafb832f57153842bdb71cee5d72678fd2e4cb43b6f3c881604b config.json | |
| e4c47b7b6aba08f948de7c2c7cb94c58dbd09dde2ded6ac185c60856ee686d27 corpus.json | |
| f53e9934693d5513a3500c4ba31462b406b4537cf23d37bfc803a962c3b94827 inference.py | |
| 1194292c6d906ac19e6f01c6ad2b42825143fad426a22aa8856d903776701ecb manifest.json | |
| cbd3e3fb4ada74d7264371b79cee7598f513b7b950f1f141ef9f1a43bd2e1b4e model.safetensors | |
| 1eee6f8afe78767ff6b9c604ccf2328c98b24bc94dbad6291a0ad699dd7345fe native_model.py | |
| a4b98c7860e40509313e719e3cd235dbf7afa7b81965d217af57416e5ff7c9bb native_tokenizer.py | |
| 2be27ef4bac5f2490b1faaf12e9487eda46356225be220139939459f5105e7af native_utils.py | |
| e871f690eabebf82da173f0e406f38b79a10cd7d2647bb3592c3fc35ae9a2f16 report.json | |
| 2d5c8eddf30346ebeffdc25fc07552d0e8a9789e967f89e7ff53dc525c7ce569 requirements.txt | |
| e095f0413a11fa91fbb58f37d00e4c6bcda59beb89b28f6f680db1436a7e8891 tokenizer.json | |
| 619f500553367eb76f5be00a98625130ec857b9c367561334984096c029ba2a9 tokenizer_config.json | |